
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "138"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 104 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 100 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.08%	0.54%	1.440704	-0.030237	-0.521713	-0.339955	-0.686501	1.420002	0.151621	0.022466	0.7686	0.7916	0.2524	3.211506	3.364306
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	0.54%	1.828969	1.739298	-0.677346	-0.377765	-0.301557	-0.071498	0.190082	3.187217	0.7243	0.7268	0.2569	2.120618	2.355459
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.024577	-0.980451	1.918281	1.183496	-0.290541	-0.583873	1.181177	-0.064064	0.0464	0.0654	0.0037	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	1.554131	2.269603	-0.669022	-0.540745	-0.634998	0.216263	-0.548229	1.503788	0.6583	0.6844	0.3748	1.613577	1.612637
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.006833	2.739525	-0.716143	-0.460981	-0.375518	0.019890	0.133646	4.602848	0.6652	0.6864	0.3660	5.355345	5.588481
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.54%	0.609294	1.280874	-0.964562	-0.278372	0.067405	-0.002306	-0.153396	0.695910	0.6951	0.7041	0.2927	1.832405	1.821969
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	2.075588	0.455135	-0.575396	-0.362244	-0.264737	-0.662431	0.161491	1.262595	0.7667	0.7617	0.2216	3.702100	3.062240
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	2.370504	2.983065	-0.349083	-0.197433	-1.596167	-0.111305	-4.675000	5.604610	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	17.84%	0.00%	1.105161	1.238849	0.823104	-0.824004	-0.225819	-1.143540	-0.862531	3.565654	0.6416	0.6682	0.3729	1.560980	1.536193
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.068494	1.301230	2.707109	-0.565918	2.071196	1.196983	-0.257970	0.459125	0.0692	0.0704	0.0084	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.317896	10.552237	2.264149	2.241005	4.933184	2.472052	46.889772	46.789806	0.6296	0.6503	0.3726	29.700314	34.327787
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	97.31%	0.00%	1.293136	2.501949	-0.448628	-0.670588	-0.216292	-1.115930	-0.498779	0.507533	0.6607	0.6832	0.3879	0.000000	0.000000
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	45.95%	0.00%	1.339771	2.690592	-0.577290	-0.423326	-0.624798	-0.227476	-0.650235	0.080999	0.6627	0.6830	0.3894	1.463036	1.440882
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	16.22%	1.600561	3.458822	-0.370916	-0.724914	-0.066253	0.049379	-0.387171	2.036406	0.6692	0.6881	0.3810	1.838980	1.728867
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	87.10%	12.90%	1.246565	2.759512	-0.771834	-0.611640	-0.395762	-0.225665	-0.328035	1.116184	0.6651	0.6855	0.3821	0.807962	0.803400
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	1.242646	2.585556	-0.536036	-0.054640	-0.198190	-0.160368	-0.280669	0.202400	0.6551	0.6772	0.3914	1.286318	1.280155
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.025589	-0.625158	0.777880	0.967807	-0.999781	-0.637466	-0.772897	2.194914	0.0613	0.0415	0.0041	nan	nan
2459577	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.611172	2.532322	-0.466961	-0.535347	-0.159992	-0.242416	-0.210390	5.196317	0.6654	0.6867	0.3799	4.088702	3.982707
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	0.00%	-0.020298	1.447970	-0.313906	-0.093058	0.859137	-0.064136	-0.098871	1.452677	0.6882	0.7093	0.3835	1.527163	1.589940
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	23.12%	-0.221181	1.096826	-0.471491	-0.412059	-0.210553	-0.517878	-0.449591	-0.425648	0.7436	0.7554	0.3314	2.038356	2.009275
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.551546	1.870429	-0.436223	-0.308205	-0.084801	-0.519788	-0.754472	0.876084	0.6812	0.7061	0.3856	1.560427	1.610361
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.733647	2.065833	-0.498227	-0.566704	0.429360	-0.717651	-0.606075	-0.322944	0.6771	0.7008	0.3909	1.618974	1.549632
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.544363	1.779815	-0.451474	-0.318561	0.024228	-0.713783	-0.362861	-0.271287	0.6901	0.7116	0.3807	1.583625	1.636976
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.010144	-0.606748	0.839599	0.840271	-0.703215	-0.624958	-0.846121	0.433735	0.0623	0.0528	0.0031	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.978267	-0.226001	0.570027	-0.333989	0.363190	-0.363711	0.008371	-0.721382	0.6378	0.6927	0.3754	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.851294	1.870939	-0.328017	-0.269523	-0.282632	-0.359059	-0.384369	-0.201759	0.7551	0.7689	0.2414	2.338372	2.058997
2459566	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	-0.894017	-0.894017	-0.869152	-0.869152	0.484019	0.484019	0.010794	0.010794	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.975321	1.502463	-0.179183	0.711444	0.007319	-0.431805	0.238396	7.079300	0.6859	0.7018	0.3942	4.183479	4.027746
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	1.265065	2.979437	0.106710	0.980657	-0.650241	0.693100	-0.174708	0.043448	0.6852	0.7053	0.3907	1.660241	1.648712
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.589267	1.102061	0.033653	0.539458	0.457945	0.429835	0.064070	0.985960	0.6807	0.7045	0.3954	1.696252	1.600977
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.217984	0.915800	-0.068581	0.149480	0.470169	-0.239795	-0.010162	0.970476	0.6900	0.7138	0.3850	1.730250	1.622197
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.889228	1.086275	0.116063	0.257595	0.811286	-0.185841	0.169442	0.377203	0.6804	0.7039	0.4001	1.697946	1.577840
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.54%	0.363163	1.319784	-0.047762	0.508312	0.679176	-0.240611	0.051689	0.621263	0.6798	0.7019	0.3867	1.775396	1.644578
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.54%	0.913723	2.476599	-0.476651	0.211265	-0.151481	-0.559416	-0.428256	2.095199	0.6787	0.6981	0.3956	1.738331	1.656007
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0563	0.0392	0.0074	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.799436	2.579282	-0.557053	0.156075	-0.066930	-0.481004	-0.476002	0.330115	0.6806	0.7014	0.3886	1.603050	1.561531
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6884	0.7087	0.3960	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6831	0.7038	0.3967	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.290416	2.141863	-0.442428	-0.403445	-0.124621	-0.073821	-0.585877	-0.467733	0.6933	0.7093	0.3905	1.331014	1.432845
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.783178	1.214238	0.022281	0.414783	0.807097	-0.371618	0.084355	1.150377	0.6883	0.7076	0.3884	1.639962	1.565404
2459550	digital_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0548	0.0774	0.0135	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.708900	2.294740	-0.341566	-0.351501	0.626846	0.140158	-0.198718	0.209606	0.6776	0.6999	0.4059	1.662342	1.662187
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.278527	2.387983	0.850275	0.425770	-0.683659	0.723640	-0.409462	14.296782	0.7351	0.7454	0.3609	3.602038	3.303333
2459541	digital_ok	100.00%	1.34%	1.34%	0.00%	100.00%	0.00%	-0.286633	1.634304	-0.853870	2.208923	-0.693363	5.166939	-0.819606	2.888975	0.7265	0.7362	0.3372	5.071578	4.745944
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.00%	0.174831	1.529540	-0.040226	0.446787	-0.411239	3.562884	-0.105028	0.296241	0.7404	0.7472	0.3461	1.651247	1.548278
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	0.283170	2.853434	0.756910	-0.121075	0.174756	2.044144	-0.612330	3.079131	0.7092	0.7209	0.4271	1.400673	1.314630
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.124861	0.948694	-0.636830	-0.407085	-0.923465	3.285529	-0.145757	1.588615	0.7752	0.7720	0.3587	1.739214	1.580146
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	-0.315650	0.607307	-0.528783	-0.514269	-0.518278	3.444455	0.457744	2.217294	0.7684	0.7621	0.3515	1.683187	1.661276
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	0.387745	2.138511	-0.416872	0.334475	0.132302	0.969739	-0.712695	-0.477207	0.7078	0.7250	0.3966	1.732319	1.576625
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.00%	1.453006	3.129983	0.486515	-0.529323	-0.358648	-0.341793	-0.855258	-0.756195	0.7114	0.7273	0.3946	1.682540	1.573690
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	5.92%	0.00%	-0.062665	0.697754	-0.655291	-0.131604	0.020278	0.092519	-0.043011	-0.264074	0.7058	0.7206	0.4133	1.513071	1.517770
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.753217	2.187792	-0.204547	0.961773	-0.797115	2.832809	0.168540	0.414630	0.9068	0.9349	0.3161	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	22.45%	0.00%	0.840789	2.641698	-0.110323	-0.807147	-0.252505	0.331911	-0.637289	-0.338054	nan	nan	nan	2.053930	1.886661
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.603685	1.374288	-0.568564	2.055855	1.037419	2.449554	-0.057173	-0.053249	0.6279	0.6906	0.3826	1.678506	1.962367
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.754629	-0.566923	1.677178	1.195252	-1.247878	1.273358	-0.790046	0.971047	0.1054	0.1048	0.0207	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.201535	1.874780	-0.493335	-0.232724	-0.202622	0.585704	-0.484290	-0.632580	0.9108	0.9473	0.1866	0.000000	0.000000
2459505	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.373170	3.954041	-0.391032	3.813749	-0.574699	7.594187	-0.276381	3.442912	0.8733	0.8432	0.2226	11.677871	16.932586
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	0.940925	1.971798	0.242668	-0.587222	0.452011	0.004870	0.127435	-0.305103	0.8782	0.8729	0.4257	0.000000	0.000000
2459501	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.498444	2.137517	-0.770324	-0.745106	-0.311871	4.169799	-0.261601	-0.750828	0.8630	0.8939	0.3858	0.000000	0.000000
2459500	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	5.26%	0.435868	1.630976	-0.627039	-0.684356	-0.630488	-1.107903	-0.675886	-0.871416	0.8650	0.8854	0.3751	0.000000	0.000000
2459499	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	0.00%	0.479477	1.786951	-0.646542	0.486014	0.160958	0.031372	-0.073172	-0.433448	0.8834	0.9413	0.1784	0.000000	0.000000
2459498	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	0.515008	1.547416	-0.324270	-0.528334	0.282463	-0.974260	-0.835315	-0.829814	0.8777	0.8867	0.4123	0.000000	0.000000
2459497	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.623426	2.153696	4.220879	-0.668919	13.408708	-1.356948	13.726741	-0.524375	0.8820	0.9293	0.3813	0.000000	0.000000
2459496	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.579382	2.089955	-0.289374	-0.534095	0.108932	-1.140824	-0.130360	-0.398077	0.1057	0.0922	0.0156	0.000000	0.000000
2459495	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.137045	1.527836	-0.439689	-0.466034	-0.008349	-0.815036	0.225599	-0.413994	0.1028	0.0922	0.0167	0.000000	0.000000
2459494	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.476904	1.785804	-0.708997	-0.224045	-0.069091	-1.065441	-0.074144	-0.694114	0.0990	0.0912	0.0167	0.000000	0.000000
2459492	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.682278	2.660674	0.404918	2.250552	1.055970	1.789721	-0.206301	0.539770	0.0909	0.0805	0.0134	0.000000	0.000000
2459491	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.727604	3.033807	0.572013	3.224611	1.636318	2.829158	-1.044334	1.516693	0.0898	0.0808	0.0114	0.000000	0.000000
2459490	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.731624	2.707663	0.347962	3.015545	0.942640	1.065534	-1.369503	0.289422	0.0829	0.0794	0.0135	0.000000	0.000000
2459489	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.021277	2.170942	0.246687	0.681511	1.143054	0.574204	-0.557346	0.409235	0.0869	0.0836	0.0137	0.000000	0.000000
2459488	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.479039	2.312315	0.125497	-1.077746	0.880859	-0.308026	-0.195213	-0.009222	0.0922	0.0812	0.0136	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 138 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 138, Node 7:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶